Method for analysing irregularities in human locomotion

ABSTRACT

The invention relates to a method of analysis for human locomotion irregularities. In such method, acceleration measurements obtained during one or more motions controlled at a stabilised gait of a human being are used, through accelerometers measuring on a time base accelerations according to at least one direction, and any locomotion irregularities are analysed from reference measurements.  
     Such measured accelerations are submitted to at least one wavelet transformation and the resulting wavelet transform is used to detect and/or analyse the irregularities.

[0001] The present invention relates to a method of analysis for humanlocomotion irregularities.

[0002] The invention concerns indeed the analysis of the human walkingin the medical and paramedical practice, more particularly to studywalking degradation and perform fall predictions so as to implementprevention measures, for example with elderly persons. The invention isalso applied to claudication measurements following traumas or medicalor surgical treatments. Another application field is sport walking orrunning.

[0003] Two complementary families of techniques have been developed tostudy the motions of the human body and the mechanics thereof and haveallowed to increase considerably the knowledge on the human locomotionphysiology:

[0004] kinematics methods that measure motions of body parts andarticular angles with the help of cinema or video images, and

[0005] dynamical methods that measure forces or accelerations acting onbody parts.

[0006] The kinematics methods are descriptive and bring numerous detailson the motions of each body segment, but are difficult to implement. Thedynamical methods are more explicative, since they give information onthe mechanical actions causing the motion. They give that way moresynthetic information which is nevertheless very fine.

[0007] Various known measuring devices implemented in the dynamicalmethods (also called kinetics) are baropedometric soles, force platformsand accelerometers. The baromedometric soles can provide a clinicianwith useful data, but are not a force-measuring device. The forceplatforms for their part give reliable and precise information, but theequipment is cumbersome and expensive. Moreover, their sizes only allowfor a single bearing, which give truncated information and does notallow for an analysis of the walking variability.

[0008] The accelerometers consisting in sensors being sensitive toinstantaneous speed variations have not the above-mentioneddisadvantages and allow to obtain precise and reliable results. Seeparticularly the article from AUVINET Bernard, CHALEIL Denis and BARREYEric, “Analyse de la marche humaine dans la pratique hospitalière parune méthode accélérométrique”, REV. RHUM. 1999, 66 (7-9), pp. 447-457(French edition) and pp. 389-397 (English edition).

[0009] Measuring accelerations has this advantage to provide sensitiveand detailed information on the forces causing motions and can be simpleto implement. Moreover, conditioning and treating data are economicalwith respect to the use of images. Further, acceleration measurementsallow to find all the kinetics and kinematics characteristics of amotion.

[0010] The treatment of the results obtained to detect and analyze anylocomotion irregularities rests generally on Fourier transforms. Theabove-mentioned article from AUVINET and al. also proposes calculationsbased on the signal autocorrelation function (study of the symmetry andregularity of the strides) completed by a z Fischer transform to reachGauss distributions.

[0011] A disadvantage of the frequency analyses (as performed by fastFourier transforms) is that they need a minimum number of points toprovide information sufficiently precise in frequency and energy.Moreover, they do not authorise a time location of a particular eventcausing a quick frequency change of the acceleration signal. Thus, it isnot possible to detect a stumble being isolated amongst other regularsteps. The frequency analysis is also badly adapted for timeirregularities of the locomotive cycle, which are not stationary, buthave chaotic properties (being impredictable at short and long term).

[0012] As far as the calculation methods based on the autocorrelationfunctions are concerned, they allow for a very good detection ofperiodic dynamical asymmetries, but are little successful to detectirregularities that are neither stationary nor periodic. They do notallow either to identify an isolated event as a sport running default ora stumble.

[0013] Moreover, several specific techniques have been developed tostudy complex motions in the equine locomotion also based onacceleration measurements. However, the motions being studied consist injumps of sport horses or gait transitions of dressage horses (changefrom trot to gallop and vice versa), hence in transient motions. Suchtechniques do not apply to stabilised gait motions, such as humanwalking or running.

[0014] The present invention relates to a method of analysis for humanlocomotion irregularities based on acceleration measurements, whichallows to detect locomotive cycles being abnormal and irregular in time.

[0015] The method according to the invention does not require a priorimathematical properties of the studied signals, such as those needed forFourier transforms (stationary state, periodicity).

[0016] Moreover, the method according to the invention may allow for aquick and simple analysis for the results obtained.

[0017] An object of the invention is also an analysis method being ableto give quantitative complementary information on the lack of dynamicalregularity in walking or running cycles.

[0018] To this end, the invention applies to a method of analysis forhuman locomotion irregularities, wherein acceleration measurementsobtained during at least one motion controlled at a stabilised gait of ahuman being are used, through at least one accelerometer measuring on atime base at least one acceleration according to at least one direction,by detecting and analysing any locomotion irregularities from referencemeasurements.

[0019] According to the invention, measured accelerations are submittedto at least one wavelet transformation and the resulting wavelettransform is used to detect and/or analyse the irregularities.

[0020] By “stabilised gait”, it is meant a repetitive approximatelyperiodic motion (such as walking or running) in contrast with atransient motion.

[0021] Surprisingly, the wavelet transformation allows to locateabnormalities with the passing time for such a stabilised gait. Even anisolated stumble may be detected amongst regular steps. Such a method isthus able to detect very short irregularities in contrast with the knownmethods based on Fourier transforms.

[0022] Preferentially, a spectrum of the wavelet transform is visualisedin three dimensions, respectively, of time, frequency and spectralenergy module of the wavelet transform to detect and analyse theirregularities.

[0023] The spectral energy module of the wavelet transform is thenadvantageously represented by coloured contours or a colour gradient.Further, a linear scale for time and a logarithmic scale for frequencyare advantageously used.

[0024] The direct visualisation of the wavelet offers a great readingsimplicity and allows to identify directly irregularities upon a graphicreading.

[0025] Preferentially, the wavelet transform is continuous, such atransform being advantageously a Morlet wavelet transform. Suchsymmetrical wavelets are well adapted for low frequency analyses, suchas body accelerations near the centre of gravity. They are given by thefollowing mathematical formula (function Ψ of variable x):

Ψ(x)=k.exp(−x ²/2).cos(ω₀ x)

[0026] wherein k is a normalisation constant and ω₀ is the pulse of thewavelet being considered, preferentially comprised between 5 and 6included. An acceleration function Acc being dependant on time t isdecomposed over a family of wavelets of such a type by the formula:${C\left( {a,b} \right)} = {\sum\limits_{- \infty}^{+ \infty}{{{{Acc}(t)}.{\Psi \left( {{at} + b} \right)}}{dt}}}$

[0027] wherein:

[0028] the coefficients C give mappings of the function Acc onto thewavelets,

[0029] the parameter a is scale parameter (modification of the waveletstretching), and

[0030] the parameter b is a translation parameter (time translation ofthe wavelets).

[0031] In a alternative embodiment, the transform being used is adiscrete wavelet transform, such as a Daubechie wavelet transform,preferably of order 5. Such last wavelets have this property to beasymmetrical and to have a zero order moment.

[0032] In a preferred embodiment, i.e. the wavelet transform having atime-dependant spectral energy, a low frequency band is defined in whichthe spectral energy for a regular locomotion is mainly concentrated andsaid irregularities are detected and/or analysed by using the spectralenergy outside such band. The frequency band is preferably comprisedbetween 1 Hz and 4 Hz.

[0033] The use of such a band makes for example possible two types ofanalysis that can be complementary:

[0034] identification of zones of spectral energy concentrations outsidethe band, and

[0035] spectral energy quantification out of such band.

[0036] Thus, advantageously, irregularities are identified and/oranalysed by locating and/or studying the spectral energy peaks outsidethe band.

[0037] Thus, for a pathological walking, high frequency peaks with asignificant energetic density exceeding the upper limit of the frequencyband (for example 4 Hz) may be observed, such peaks being more or lessperiodical and regular in time and frequency. Graphic irregularities inthe shape of such peaks are indicative of alterations in walkingdynamics and regularity.

[0038] Moreover, a locomotion irregularity degree is advantageouslycalculated by reporting the margin spectral energy outside the band tothe total spectral energy.

[0039] Consequently, for a pathological walking, it can be consideredthat the margin energy above 4 Hz is higher than 6%.

[0040] In another advantageous analysis type based on the low frequencyband, the located spectral energy values exceeding the band aremeasured, for example in the case of very altered walking with highfrequency patterns and quite various shape and energy.

[0041] The analyses with a wavelet transform are advantageouslyassociated with other methods giving complementary information in themethod of analysis for human locomotion irregularities. Such informationis combined with the results obtained through wavelet transforms so asto specify the use mode for wavelets, interpret the results obtained bysuch wavelets and/or complete the results extracted from the analyseswith wavelets.

[0042] Thus, in an advantageous embodiment, the analysis methodsaccording to any one of the accelerations are at least in a number oftwo and to detect and/or analyse the irregularities, at least onevectogram (called “front butterfly”) is also used representing theintensity of one of the measured accelerations depending on theintensity of another of the measured accelerations. Chaotic drifts maybe thus identified visually with respect to a stationary and periodicrate.

[0043] The accelerations used are preferentially a vertical accelerationand a lateral acceleration of the subject.

[0044] In a preferred implementation, to detect and/or analyse theirregularities, at least one representation in a phase space is alsoused, giving the intensity of at least one of the accelerationsdepending on the intensity of the time derivative of such accelerationor depending on such acceleration time shifted with a predeterminedtime.

[0045] The movement of a stationary and periodic rate towards a chaoticrate can be also identified visually. Advantageously, the accelerationbeing studied is a vertical acceleration.

[0046] In such preferred embodiment, at least one Lyapunov coefficientof the representation in the phase space, preferably the maximumLyapunov coefficient, is calculated advantageously. Thus, dynamicallocomotion irregularities can be quantified, such a coefficientmeasuring the deviating speed of the acceleration signal orbits withinthe phase space.

[0047] Thus, the maximum Lyapunov coefficient, being calculated on acranio-caudal accelerometric signal measured at the level of the centreof gravity of a subject, quantifies globally a lack of dynamicalregularity in the walking cycles. It allows to detect temporal anddynamical fluctuations of co-ordination with respect to a risk of fall.Such global measurement of walking regularity has a clinical interestfor a prediction of the risk of fall with an elderly subject. Walkingcan be thus considered as pathological when the Lyapunov coefficientbeing used is higher than a critical value.

[0048] Thus, advantageously, it is considered that a co-ordinationdisorder occurs if the maximum Lyapunov coefficient is higher than 0.4.

[0049] The device used for the accelerometers and the accelerationmeasurements is preferentially conform to what is mentioned in thearticle from AUVINET et al. supra. In particular, the analysis methodadmits advantageously the following characteristics, consideredseparately or according to all their technically possible combinations:

[0050] the accelerations are measured with the help of an accelerometricsensor comprising two accelerometers arranged according to perpendicularaxes;

[0051] the accelerometers are incorporated into a semi-elastic waistbandsecured to the waist of the subject so that they are applied in themedian lumbar region opposite the intervertebral space L3-L4;

[0052] the accelerometers are arranged near the centre of gravity of thesubject (this latter being located when the person is standing at restbefore the second sacral vertebra);

[0053] the accelerations are measured according to cranio-caudal andlateral axes of the subject called hereunder, respectively, vertical andlateral axes;

[0054] the acceleration measurements are recorded with a portablerecorder;

[0055] the results obtained with the wavelet transform are completed byobtaining the stride frequency, the step symmetry and regularity, thesymmetry and regularity variables being advantageously submitted to a zFischer transform;

[0056] the motions controlled at a stabilised gait consist in a freewalking at a comfort speed for the subject over a distance comprisedbetween 25 and 50 m, preferably equal to 40 m, advantageously go andback.

[0057] Moreover, the following characteristics are advantageouslyimplemented separately or in combination:

[0058] the accelerations are thus measured according to a thirdaccelerometer, the three accelerometers being arranged according toperpendicular axes corresponding respectively to the cranio-caudal,lateral and longitudinal (antero-posterior) axes of the subject;

[0059] for the analysis of sport motions, the accelerations measured arecranio-caudal and longitudinal (sagittal plane of the subject)accelerations;

[0060] for the analysis of sport motions, measurements are performedduring effort tests on track or on conveyor belt;

[0061] a complementary Fourier frequency analysis is also implemented inthe analysis method by calculating:

[0062] the total spectral energy (it decreases for a pathologicalwalking),

[0063] the relative energy of the fundamental frequency whichcorresponds to the step frequency (it decreases and is distributedtowards higher frequency harmonics for a pathological walking) and/or

[0064] the spectrum slope calculated by a linear regression equation ona semi-logarithmic representation of the squared spectral energydepending on the frequency (such slope is all the more negative sincewalking is altered in its temporal and dynamical regularity).

[0065] Moreover, a sensor comprising the accelerometers, a recorder andan event-marking device provided to mark events on the recordedacceleration signals and/or synchronize the acceleration recordings withother measuring devices (video or cinema camera, force platform, timingcell, EMG device, etc.) are advantageously used. Such device isadvantageously arranged between the sensor and the recorder andadvantageously integrated into a recording housing of the recorder.

[0066] The event-marking device has preferably activation inputsconsisting in manual electrical, optoelectronic (cell sensitive to alight flash) and/or electronic contacts.

[0067] It has preferably outputs consisting in:

[0068] a square wave emitter having advantageously a maximum value(saturation) of a period comprised between 10 and 50 msec and preferably10 msec for a signal acquisition frequency at 100 Hz advantageouslyemitted on the acceleration signal tracks so as to mark such signal on ameasuring point;

[0069] a plurality of red luminescent diodes (LED) being lightened toprovide a light signal visible on a single image with a video camera,advantageously during a period comprised between 10 and 50 msec,preferably equal to 10 msec; and/or

[0070] a TTL output providing advantageously a squared signal of 5Vduring a period comprised between 10 and 50 msec, preferably equal to 10msec.

[0071] The use of such an event-marking device is of a particularinterest to mark change times (start and arrival in a walking or runningtest) on acceleration recordings. Thus, a spatial or cinematic marking(synchronized video film) of the recorded motion can be obtained so asto calculate the representative distances for a sport motion.

[0072] The invention also relates to the applications of the method:

[0073] in the medical field, in particular for the early detection ofneuromotor disorders of elderly subjects (it is then particularlyinteresting to use the maximum Lyaponov coefficient), and

[0074] in the sport field, in particular for detecting technical defectsfor sport walkers or runners.

[0075] Thus, the method of the invention can be advantageously appliedto the detection of particularities of the athlete's stride during arace period, such as the flight time, the bearing time, the symmetry ofright and left half-strides, the right and left propulsion and brakingforces and the race fluidity index.

[0076] The method according to the invention can also be applied tomeasure and quantify the biomechanical constraints due to a disease ofthe locomotive apparatus, including as a function of the race speed.

[0077] The characteristic of the particularities of the athlete's strideduring the race according to the method of the invention is usefulparticularly to improve the efficiency and performance of the sportsman.

[0078] The characterization of the acceleration parameters according tothe method of the invention is useful particularly to assess thetolerance for a disease in the locomotive apparatus as a function of theworking loads.

[0079] The present invention will be illustrated and better understoodwith particular embodiments, with no limitation, referring to theaccompanying drawings, wherein:

[0080]FIG. 1 represents a subject provided with an acceleration sensorfor implementing the analysis method according to the invention;

[0081]FIG. 2 shows a measuring device useful to implement the analysismethod of the invention and corresponding to FIG. 1;

[0082]FIG. 3 shows an enlarged part of the measuring device of FIG. 2,with no sensor;

[0083]FIG. 4 represents a front sectional view of the sensor of themeasuring device of FIG. 2;

[0084]FIG. 5 represents a top sectional view of the sensor of themeasuring device of FIGS. 2 and 4;

[0085]FIG. 6 shows vertical and lateral acceleration curves in the timesynchronized with images of a subject for a regular walking stride;

[0086]FIG. 7 shows vertical and lateral acceleration curves in the timesynchronized with images of a subject for a pathologic walking stride;

[0087]FIG. 8 represents evolution curves in the time for vertical andlateral accelerations for a regular walking of a subject;

[0088]FIG. 9 shows a three dimension spectral image of a Morlet wavelettransform obtained from the vertical acceleration of FIG. 8 according tothe analysis method of the invention;

[0089]FIG. 10 shows an enlarged spectral image of FIG. 9;

[0090]FIG. 11 represents a curve giving the evolution as a function oftime of the vertical acceleration for a pathological walking of afalling elderly subject;

[0091]FIG. 12 shows a three dimension spectral image of a Morlet wavelettransform obtained from the vertical acceleration of FIG. 11 accordingto the analysis method of the invention;

[0092]FIG. 13 is a vectogram so-called “front butterfly” giving thevertical acceleration as a function of the lateral accelerationcorresponding to the regular walking associated with FIGS. 8 to 10;

[0093]FIG. 14 represents a diagram of the vertical acceleration phasesgiving the acceleration first derivative in the time as a function ofthe temporal series of such acceleration for a regular walkingcorresponding to FIGS. 8 to 10;

[0094]FIG. 15 represents a diagram of the vertical acceleration phasesgiving the acceleration first derivative in the time as a function ofthe temporal series of such acceleration for a pathological walkingcorresponding to FIGS. 11 and 12;

[0095]FIG. 16 shows a three dimension spectral image of a Morlet wavelettransform obtained according to the method of the invention in a runningperiod for a sportsman the stride of whom is very fluidic;

[0096]FIG. 17 shows a three dimension spectral image of a Morlet wavelettransform obtained according to the method of the invention in a runningperiod for a sportsman having a strong propulsion and braking asymmetry.

[0097] On FIGS. 16 and 17, the upper graph shows the vertical,longitudinal and lateral acceleration curves in the time expressed inseconds; the median graph shows the spectral image of the wavelettransform of the vertical motions in the time; the lower graphillustrates the spectral image of the wavelet transform of thelongitudinal motions in the time expressed in seconds.

[0098]FIG. 18 shows vertical, antero-posterior and lateral accelerationcurves in the time synchronized with images of a subject for a regularrunning stride.

[0099] A measuring device 10 (FIGS. 1 and 2) comprises amotion-sensitive sensor 16 connected with an ambulatory recorder 20formed as a housing.

[0100] The sensor 16 is an accelerometric sensor comprising (FIGS. 4 and5) accelerometers 201, 202 and 203 arranged perpendicularly so as todetect accelerations respectively in the perpendicular directions 21, 22and 23. As an illustration, the three accelerometers 201, 202 and 203are oriented respectively according a first cranio-caudal direction 21(hereafter vertical), according to a second latero-lateral direction 22and according to a third antero-posterior direction 23 (hereafterlongitudinal) of a subject 1. Such accelerometers 210-203 are sensitiveto continuous and dynamical components and are adapted for measuring lowfrequency motions. Their own frequency is for example 1200 Hz, themeasuring range being between ±10 g and the sensitivity being 5 mV/g at100 Hz. They form a cubic assembly 205 in which their orthogonality isprovided through squares 240. Such assembly 205 is moulded in asmall-size parallelepiped polymer coating 200. The coating 200 isinsulating, stiff, protective and waterproof. It has for example aheight h (direction 21), a width I (direction 22) and a depth p(direction 23) respectively of 40, 18 and 22 mm.

[0101] Each of the accelerometers 201, 202 and 203 is provided with fiveconnecting terminals comprising:

[0102] a mass terminal (terminals 210 and 220 respectively of theaccelerometers 201 and 202),

[0103] a negative power supply terminal connected with the mass terminal(terminals 211 and 221 respectively of the accelerometers 201 and 202),

[0104] a positive power supply terminal (terminals 212 and 222respectively of the accelerometers 201 and 202),

[0105] a negative signal terminal (terminals 213 and 223 respectively ofthe accelerometers 201 and 202), and

[0106] a positive signal terminal (terminals 214 and 224 respectively ofthe accelerometers 201 and 202).

[0107] The terminals and the accelerometers 201 to 203 are supportedrespectively on ceramic bases 206 to 208.

[0108] The mass terminals and the negative power supply terminals of thethree accelerometers 201 to 203 are interconnected.

[0109] The sensor 16 also comprises a wire 251 with twelve mass braidedconductors (metal braid avoiding interference) containing wiresoriginating from the power supply and signal terminals of the threeaccelerometers 201 to 203. Such wire 251, leading to the recorder 20, ispartly surrounded by a semi-rigid thermoretractable sheath 250 enteringthe coating 200. Such sheath 250 allows to avoid any breaking of thewire. It is moreover bounded outside the coating 200 by a frusto-conicalpart 252 integral with the coating 200 so as to avoid wire breakingrisks. The frusto-conical part 252 has a length L for example equal to25 mm.

[0110] Moreover, the wire 251 is provided with a clip 253 adapted tocome in abutting relationship with the sheath 250 in case of the wire251 sliding in the sheath 250 so as to avoid to tear off the welds.

[0111] The sensor 16 is incorporated into a semi-elastic waistband 11secured to the waist of the subject 1 so that this sensor 16 applies inthe median lumbar region opposite the intervertebral space L3-L4. Suchpositioning of the sensor 16 allows for a good stability of theaccelerometers near the centre of gravity of the subject 1, such centreof gravity being located before the second sacral vertebra for a humanbeing standing at rest.

[0112] The sensor 16 is arranged within a space 17 formed by a slackpart 12 of the waistband 11 and by a leather reinforcement 13 appliedagainst the waistband 11 through wedges 14 and 15 made in a high densityfoamed fabric arranged on either side of the part 12 (FIGS. 2 and 3).The wedges 14 and 15 allow to position the coating 200 of the sensor 16precisely within the vertebral groove of the subject 1.

[0113] The recorder 20 has for example an acquisition frequency of 100Hz and an autonomy allowing for a continuous recording during 30minutes. It is provided with a low-pass filter with a cutout frequencyof 50 Hz to avoid any folding phenomena. It receives three perpendicularsynchronized tracks. The acceleration measurements are digitized andcoded for example on 12 bits.

[0114] The recorder 20 comprises connection means with a treatment unit,for example a PC type computer through a serial communication port withthe help of a transfer software.

[0115] The measuring device 10 also comprises an event-marking device 25electrically arranged between the sensor 16 and the recorder 20 andintegrated into the housing of the recorder 20 (FIG. 2). Such device 25is adapted for marking events on recorded signals and/or synchronizeacceleration recordings with other measuring devices. It is providedwith a cell sensitive to a light flash and is adapted for saturating theaccelerometric signal for example during 0.01 second for an acquisitionat 100 Hz at the time where a flash is triggered.

[0116] In operation, the subject 1 is asked to walk on a straight linefor example over a distance of 30 m motion and 30 m back. Timing cellsare advantageously used, distant by 30 m from the ends of the pathfollowed by the subject so as to be able to measure speeds andsynchronize measurements. Such timing cells form each an infraredbarrier comprising and emitter and a receiver. They are respectivelyconnected with flashes that are triggered when the infrared barrier iscrossed by the subject 1 and are detected by the event-marking device25.

[0117] Thus, the vertical and lateral accelerations are obtained in thetime for a subject 60 having a regular walking stride (FIG. 6) and asubject 90 having a pathological walking stride (FIG. 7)

[0118] Thus, for regular walking, a curve 40 is represented giving thevertical acceleration (axis 32, g) and a curve 50 giving the lateralacceleration (axis 33, g) in the time (axis 32, sec). On these curves 40and 50 different walking steps are identified and they can be put inrelation with synchronized images (taken by a video camera) of thewalking subject 60. Thus, on the curves 40 and 50:

[0119] zones 41 and 51 corresponding to an application of the left leg90 a (image 61),

[0120] zones 42 and 52 corresponding to a bipodal bearing (image 62),

[0121] zones 43 and 53 corresponding to a lift of the right leg 90 b anda flexion of the right knee (image 63),

[0122] zones 44 and 54 corresponding to a vertical unipodal bearing ofthe left leg 90 a (image 64), and

[0123] zones 45 and 55 corresponding to a unipodal push of the left leg90 a (image 65),

[0124] are marked respectively.

[0125] Similarly, the curves 70 and 80 (FIG. 7) representingrespectively the vertical and lateral accelerations in the time, on thecurves 70 and 80:

[0126] zones 71 and 81 corresponding to an application of the right leg90 b (image 91),

[0127] zones 72-and 82 corresponding to a bipodal bearing (image-92),

[0128] zones 73 and 83 corresponding to a lift of the left leg 90 a anda flexion of the left knee (image 93),

[0129] zones 74 and 84 corresponding to a vertical unipodal bearing ofthe right leg 90 b (image 94), and

[0130] zones 75 and 85 corresponding to a unipodal push of the right leg90 b (image 95),

[0131] are identified respectively.

[0132] The method of analysis for human locomotion irregularities basedon such results will be described now more in detail. Thus, theattention is drawn onto the curves 100 and 110 (FIG. 6) givingrespectively the vertical and lateral accelerations (g, single axis 34with a translation of −1 g for lateral acceleration) in the time (sec,axes 31 and 35 respectively for vertical and lateral accelerations andreference axis 36). Thus, cycles 101-105 are identified on the verticalacceleration curve 100 and cycles 111-115 on the lateral accelerationcurve (FIG. 6) being synchronized and corresponding respectively tomotions of the left and right steps.

[0133] Similarly, a curve 140 (FIG. 9) is obtained for example, givingthe vertical acceleration (axis 32) in the time (axis 31) for apathological walking, such curve 140 admitting also cycles 141-145.

[0134] The vertical acceleration for regular walking and forpathological walking is submitted to a continuous wavelet transform, forexample of Morlet type. Such transform is generally expressed by:${C\left( {a,b} \right)} = {\sum\limits_{- \infty}^{+ \infty}{{{{Acc}(t)}.{\Psi \left( {{at} + b} \right)}}{dt}}}$

[0135] wherein:

[0136] the variable t is time,

[0137] Acc(t) is the vertical acceleration signal,

[0138] the function ψ(at+b) is the wavelet function used for example ofMorlet,

[0139] the parameters a and b of the wavelet function are respectivelythe stretching (or scaling) parameter and the wavelet translationparameter, and

[0140] the coefficients C(a, b) are the coefficients for the continuouswavelet transform.

[0141] From the coefficients C (a, b), for a regular walking, a threedimension spectral image 120 (FIGS. 7 and 8) is built up, correspondingrespectively to time, frequency and spectral energy. Such image 120 isrepresented in a plane time-frequency (or time-scale) with a linearscale for time (axis 3, sec) and a semi-logarithmic scale for frequency(axis 37, Hz). The wavelet energy module (g²) is represented by colouredcontours.

[0142] Similarly, a spectral image 150 of the Morlet wavelet transformof the vertical acceleration (FIG. 10) is obtained for pathologicalwalking corresponding to FIG. 9.

[0143] The energy density on the enlargement 130 and the spectral image150 is expressed in acceleration (g²).

[0144] On the spectral images 120 and 150 is defined a low frequencyband having lower 121 and higher 122 limits associated with values of 1Hz and 4 Hz. In the case of a regular walking, the spectrum energydensity is mainly concentrated within such band. A regular zone 123comprised within the frequency band and a pathological zone 124 locatedabove this band are more precisely defined.

[0145] It is observed in fact that, for a regular walking of the subject(FIG. 7 and enlargement 130 of FIG. 8), the spectral energy isessentially concentrated in the zone 123, periodical low peaks 125-128appearing in the pathological zone 124 and corresponding to walkingcycles of the subject 60.

[0146] In contrast, on the spectral image 150 associated with thepathological walking of the subject 90,

[0147] a fall of the total spectral energy,

[0148] high frequency peaks 155-158 having a significant energy densityexceeding the limit 122 of 4 Hz, and

[0149] such peaks 155-158 having periodicity and regularity alterationsin time and frequency,

[0150] are observed.

[0151] The peaks 155-158 can be interpreted by stating that the moregraphically irregular their shape, the more altered are the walkingdynamics and regularity.

[0152] Besides this graphic information, quantitative information iscalculated as explained hereafter. In particular, on the spectral images120 and 150, the total quantity of spectral energy as well as the marginspectral energy on the frequency axis 37 corresponding to an energyhigher than the limit 122 of 4 Hz are determined. The data beingaccumulated on reference cases show that this margin energy is higherthan 6% for a pathological walking.

[0153] Thus, for the regular walking of example (FIGS. 7 and 8) a totalenergy of 22.97 g² and a margin energy representing 5.3% are obtainedand, for the pathological walking (FIG. 10) a total energy of 2.50 g²and a margin energy representing 15.4%.

[0154] In the case of a quite altered walking with high frequencypatterns of very different form and energy, the punctual value of theenergy module is measured on the spectral image.

[0155] Other analysis techniques complete preferably the so-obtainedinformation. In particular, it is interesting to go deeper into theinformation obtained by a frequency analysis and a calculation of thesymmetry and regularity through the autocorrelation function asindicated in the article from AUVINET et al. supra. That way:

[0156] a stride frequency of 1.00 stride per second,

[0157] a stride symmetry of 95.01% with a z Fischer transform of 183.25,and

[0158] a stride regularity of 186.27 (on 200) with a z Fischer transformof 337.56 are obtained for the regular walking supra.

[0159] A stride frequency of 0.88 stride per second (too a slowfrequency),

[0160] a stride symmetry of 95.57% with a z Fischer transform of 189.35,and

[0161] a stride regularity of 160.93 (on 200) with a z Fischer transformof 222.41 (abnormal regularity)

[0162] are obtained for the pathological walking of the example (FIGS. 9and 10).

[0163] Such results are coupled with those obtained with the wavelettransforms. In particular, the peaks 125-128 or 155-158 are put inrelationship with the stride frequency, their right/left alternationwith symmetry and their similarity with the stride regularity.

[0164] Advantageously, such results obtained with wavelet transforms arealso completed through a vectogram (“front butterfly”) such as the one160 (FIG. 11) obtained for the regular walking of the example (FIGS. 6to 8) giving the vertical acceleration (axis 32, g) as a function of thelateral acceleration (axis 33, g). On the front butterfly 160 cyclicpaths 161-163 are observed that have a tendency to be all the moresuperimposed in each other since the walking is regular. In pathologicalcases, such cyclic paths deviate from each other in an identifiable andanalysable manner.

[0165] The analysis is also completed by a phase diagram giving forexample for the vertical acceleration for regular walking (FIG. 12) andthe pathological walking (FIG. 13) given as an example supra, theacceleration derivative (g.s⁻¹, axis 38) as a function of theacceleration (g, axis 32). Thus, diagrams 170 and 180 correspondingrespectively to regular and pathological walking and having respectivelycyclic paths 171-173 and 181-183 are obtained.

[0166] The irregular walking of a falling object for example isreflected by a chaotic rate revealed qualitatively by a more pronounceddivergence of the cyclic paths 181-183.

[0167] The divergence rate of the cyclic paths (or orbits) of the signalis quantified through at least one Lyapunov coefficient. Such acoefficient assesses the sensitivity of a system to deviate from astationary and periodical regimen from a particular point at a time towhere the system characteristics (starting conditions) are known.

[0168] The mean Lyapunov coefficient λ_(m) of N−1 Lyapunov coefficientsobtained with a sampling interval equal to 1 is conventionally given bya formula as follows:$\lambda_{m} = {\frac{\log \quad 2}{N - 1}{\sum\limits_{n = 1}^{N - 1}\left( \frac{1_{n + 1}}{1_{n}} \right)}}$

[0169] wherein:

[0170] N is the number of points considered,

[0171] n is the current point number, and

[0172] l_(n) represents the distance between the indexed point nbelonging to an orbit and a neighbour closest point belonging to aneighbour orbit in the phase space

[0173] A practical method for an algorithmic calculation of such acoefficient is for example found in the article from WOLF et al.“Determining Lyapunov exponents from a time series”, in review Physica,16D, 1985, pp. 285-317.

[0174] As an example, for a phase space dimension equal to 3 and asampling interval equal to 3 points, the maximum Lyapunov coefficientcalculated on the vertical accelerometric signal is comprised between 0and 1, such coefficient being all the higher since the pathologyincreases. Thus, for a value higher than 0.4, walking is quiteirregular. This coefficient quantifies the lack of dynamical regularityof the walking cycles and detects the temporal and dynamicalfluctuations of co-ordination, in relation with a fall risk.

[0175] The whole results obtained from such different methods focussedonto the wavelet analysis are combined to identify the locomotionirregularities and quantify them.

[0176] The above-mentioned examples based on the vertical and lateralaccelerations for walking are particularly adapted for identifyingclaudication or degradation of the capacities of elderly people so as toscreen early fall risks and to take prevention measures.

[0177] In other embodiments, other gaits are taken into considerationand/or other accelerations are used. Thus, for example, a sport walkingis analysed by using advantageously the vertical and antero-posterioraccelerations of the rachis (sagittal plane of the subject, directions21 and 23). Preferably, technical defects of a sport walking athlete arethus detected, including a knee flexion at the time of the applicationand/or a simultaneous broken contact with the ground of both athlete'sfeet.

[0178] In another embodiment, a sport race is analysed. FIGS. 16 and 17illustrate the three dimensional spectral image of a wavelet transformobtained respectively with a sportsman having a fluidic stride (FIG. 16)and a sportsman having an irregular stride (FIG. 17). FIG. 17 shows onthe longitudinal acceleration trace (upper curve <fre pro>) and thecorresponding wavelet spectrum (below), a high frequency peak at 0.2-0.4sec corresponding to an abrupt braking deceleration at the time of theapplication of the left foot. At the time 0.6-0.8, the application ofthe right foot does not show any abrupt braking, but a more pronouncedpropulsion that on the left. Such analyses of the stride allow tovisualize and characterize the stride asymmetries and to determineconsequently the biomechanical characteristics of an athlete's stride. Acomparison of the stride characteristics with the same athlete analysedat two given moments being distant in the time allow to identify thepresence of stride abnormalities characterized by changes in his or herbiomechanical characteristics. The stride analyses with said athleteaccording to the method of the invention also allow to quantify and thusto check the functional tolerance of a disease in the locomotiveapparatus of the sportsman.

[0179] In such embodiment of the method of the invention for theanalysis of a sport race, FIG. 18 shows the vertical, antero-posteriorand lateral acceleration curves in the time synchronized with the imagesof the subject.

[0180] On those curves

[0181] the bearing moment (300) of the left leg (G),

[0182] the mid-bearing moment (301) of the left leg (G),

[0183] the push moment (302) of the left leg (G), and

[0184] the end-bearing moment (303) of the left leg (G)

[0185] may be identified.

1. Method of analysis for human locomotion irregularities, whereinacceleration measurements obtained during at least one motion controlledat a stabilised gait of a human being are used, through at least oneaccelerometer measuring on a time base at least one accelerationaccording to at least one direction, by detecting and analysing anylocomotion irregularities from reference measurements, characterized inthat said measured accelerations are submitted to at least one wavelettransformation and the resulting wavelet transform is used to detectand/or analyse said irregularities, and the spectrum of said wavelettransform is visualised in three dimensions, respectively, of time,frequency and spectral energy module of the wavelet transform to detectand analyse said irregularities.
 2. Method of analysis according toclaim 1, characterized in that the spectral energy module of the wavelettransform is represented by coloured contours or a colour gradient. 3.Method of analysis according to claim 2, characterized in that a linearscale for time and a logarithmic scale for frequency are used.
 4. Methodof analysis according to any one of preceding claims, characterized inthat said wavelet transform is continuous.
 5. Method of analysisaccording to claim 4, characterized in that said transform is a Morletwavelet transform.
 6. Method of analysis according to any one ofpreceding claims, characterized in that the wavelet transform having atime-dependant spectral energy, a low frequency band is defined in whichthe spectral energy for a regular locomotion is mainly concentrated andsaid irregularities are detected and/or analysed by using the spectralenergy outside such band.
 7. Method of analysis according to claim 6,characterized in that the band is comprised between 1 Hz and 4 Hz. 8.Method of analysis according to any one of claims 6 or 7, characterizedin that irregularities are identified and/or analysed by locating and/orstudying the spectral energy peaks (124-128, 155-158) exceeding saidband.
 9. Method of analysis according to any one of claims 6 to 8,characterized in that a locomotion irregularity degree is calculated byreporting the margin spectral energy outside said band to the totalspectral energy.
 10. Method of analysis according to any one ofpreceding claims, characterized in that said accelerations are at leastin a number of two and in that, to detect and/or analyse theirregularities, at least one front butterfly (160) is also usedrepresenting the intensity of one of the measured accelerationsdepending on the intensity of another of the measured accelerations. 11.Method of analysis according to any one of preceding claims,characterized in that, to detect and/or analyse the irregularities, atleast one representation (170, 180) in a phase space is also used,giving the intensity of at least one of said accelerations depending onthe intensity of the time derivative of said acceleration or dependingon said acceleration time shifted with a predetermined time (d). 12.Method of analysis according to claim 11, characterized in that at leastone Lyapunov coefficient of said representation in the phase space,preferably the maximum Lyapunov coefficient, is calculated.
 13. Methodof analysis according to claim 12, characterized in that it isconsidered that a co-ordination disorder occurs if the maximum Lyapunovcoefficient is higher than 0.4.
 14. Application of the method ofanalysis according to any one of preceding claims for the medical field,in particular for the early detection of neuro-motor disorders ofelderly people.
 15. Application of the method of analysis according toany one of claims 1 to 13 for the sport field, in particular fordetecting technical defects with sport walkers or runners. 16.Application of the method of analysis according to any one of claims 1to 13 for detecting athlete's stride particularities during a raceperiod.
 17. Application of the method of analysis according to any oneof claims 1 to 13 for measuring and quantifying the biomechanicalconstraints associated to a disease of the locomotive apparatus.